Common acoustic environments are often complex mixtures of sounds from multiple acoustic sources. Some of these sources contain critical information listeners need to comprehend;others are distractions that interfere with listeners6 comprehension. As the US population ages, a significant and growing segment have difficulty coping with such complex sound fields. Current solutions are limited to hearing aids which notoriously amplify all acoustic sources, or headsets which selectively amplify a single source but passively or actively isolate the listener from the rest of his or her acoustic environment. We propose to develop a product called ACES (Acoustic Component Enhancement System) to help listeners by presenting them with a virtual sound field reconstructed from their actual acoustic environment in such a way that certain sources2called traceable sources2are enhanced (if they listener wants to attend to them) or suppressed (if they are distracting). Traceable sources are acoustic sources for which some kind of pre-acoustic information, or trace, exists that can be used to identify and isolate the sound of the source. To isolate the sound, ACES will use a novel knowledge-based component called a Source Hypothesis Generator (SHG). STAR has identified important classes of common traceable sources for which such SHGs can be constructed. For instance, any sound produced by a loudspeaker is traceable. In this important case, the speaker is the acoustic source, and the electrical signal that drives the speaker is its trace. If a traceable source is informative, ACES creates an enhanced version of it in the virtual sound field that ACES constructs for the listener. To do so, ACES must suppress the original acoustic representation (which may be distorted and difficult to comprehend) and replace it with a more 3listener-friendly4 version. ACES can enhance the reconstructed sound by playing it louder, time-shift it, repeat it, and play it slower or faster. STAR has extensive experience implementing state-of-the-art Blind Source Separation algorithms to separate independent acoustic sources from the mixed responses of multiple microphone signals. In Phase I, we plan to extend those algorithms in novel ways to leverage the independent knowledge-based information represented by the ACES acoustic source hypotheses. In effect, we will 3scrub4 the contributions of the traceable sources out of the physical microphone responses to the sound field. The result should be improved separation of both traceable and un-traceable 3hidden4 sources, in a system that requires fewer microphones, and meets users6 needs better. We will also carry out a perceptual experiment to test the intelligibility of spoken words presented in a noisy environment, when the traceable speech source has been enhanced as described above, or when its masking noises have been suppressed using the ACES technology. We anticipate that integrating ACES with assistive listening devices and hearing aids will require three or four years of further development beyond this Phase I project, and require $1-2 million in supporting funds. PUBLIC HEALTH RELEVANCE: The proposed project supports the development of ACES (Acoustic Component Enhancement System), a product to enhance the quality of life of hearing-impaired listeners, including many aging baby-boomers: such listeners have difficulty processing complex acoustic environments in which some acoustic sources contain critical information they need to comprehend, and other sources are distractions that interfere with comprehension. ACES uses novel filtering techniques to suppress distracting sources and enhance information-bearing ones (for example, announcements). ACES employs these techniques to generate a listener-controlled "virtual reality" in which distractions are suppressed and important sounds are enhanced.